Predicting Mortality and Algorithmic Fairness of ICU Patients

Abstract

Predicting mortality for ICU patients while ensuring fairness across different demographic groups is a multifactorial issue. This study aims to address this challenge by leveraging the Medical Information Mart for Intensive Care (MIMIC-IV) dataset to develop robust machine learning models. The study compares neural network and logistic regression models using both a comprehensive set of predictors and a subset of the most significant predictors. Bias mitigation techniques, including reweighting and threshold modification, were applied to address disparities in model performance. Results indicate that while overall accuracy was high, significant biases were observed, particularly against Asian patients and Medicaid insurance holders. The logistic regression model trained on a balanced dataset and adjusted through threshold modification emerged as the optimal choice, achieving minimal inequalities across subgroups while maintaining high accuracy and F1 scores for mortality prediction. These findings underscore the need for continuous evaluation and advanced bias mitigation strategies to ensure equitable healthcare outcomes.